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ISACA AAISM Exam Syllabus Topics:

Topic
Details

Topic 1

AI Technologies and Controls: This section of the exam measures the expertise of AI Security Architects and assesses knowledge in designing secure AI architecture and controls. It addresses privacy, ethical, and trust concerns, data management controls, monitoring mechanisms, and security control implementation tailored to AI systems.

Topic 2

AI Governance and Program Management: This section of the exam measures the abilities of AI Security Governance Professionals and focuses on advising stakeholders in implementing AI security through governance frameworks, policy creation, data lifecycle management, program development, and incident response protocols.

Topic 3

AI Risk Management: This section of the exam measures the skills of AI Risk Managers and covers assessing enterprise threats, vulnerabilities, and supply chain risk associated with AI adoption, including risk treatment plans and vendor oversight.

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q199-Q204):

NEW QUESTION # 199
Embedding unique identifiers into AI models would BEST help with:

A. Detecting adversarial attacks
B. Eliminating AI system biases
C. Preventing unauthorized access
D. Tracking ownership

Answer: D

Explanation:
The AAISM framework explains that embedding unique identifiers-such as digital watermarks or model fingerprints-enables organizations to trace and verify model provenance. This technique is used for tracking ownership and intellectual property rights over models, particularly when sharing, licensing, or distributing AI systems. While identifiers may support certain security functions, their primary control objective is ownership verification, not preventing access, bias removal, or adversarial detection. The correct alignment with AAISM controls is tracking ownership.
References:
AAISM Exam Content Outline - AI Technologies and Controls (Model Provenance and Watermarking) AI Security Management Study Guide - Ownership and Accountability of Models

NEW QUESTION # 200
An organization plans to leverage AI in the software development process to speed up coding. Which of the following should the information security manager do FIRST?

A. Perform a cost-benefit analysis
B. Conduct an impact assessment
C. Train developers to verify AI output
D. Update the security policy to include AI controls

Answer: B

Explanation:
AAISM guidance specifies that before introducing AI into any business or technical workflow, an AI Impact Assessment must be conducted early to determine potential risks, privacy implications, misuse scenarios, governance gaps, and required security controls. This aligns with the principle that AI adoption must begin with governance and risk identification, not training or policy modification.
Training developers (B) is important but occurs after identifying risks. Updating policies (C) is also downstream of the assessment. Cost-benefit analysis (D) supports business justification but does not address security.
References: AAISM Study Guide - AI Governance; Impact Assessment Required Before Deployment.

NEW QUESTION # 201
To ensure ethical and responsible AI use, which AI usage policy metric is MOST important to monitor?

A. Frequency of policy reviews and updates
B. Frequency of policy consultations by employees
C. Number of policy violations
D. Number of AI projects reviewed for compliance

Answer: B

Explanation:
AAISM states the most meaningful policy performance metric is how often employees consult AI policies, which reflects:
* awareness
* practical adoption
* reliance on policy guidance
* safe decision-making behavior
Violations (A) are lagging indicators. Compliance reviews (B) measure oversight, not behavior. Policy review frequency (D) tracks governance updates, not usage.
References: AAISM Study Guide - AI Policy Effectiveness Metrics.

NEW QUESTION # 202
Which of the following BEST addresses risk associated with hallucinations in AI systems?

A. Recursive chunking
B. Human oversight
C. Automated output validation
D. Content enrichment

Answer: B

Explanation:
AAISM prescribes human-in-the-loop (HITL) controls as the primary safeguard for high-impact generative AI use cases to mitigate hallucination risk. Human oversight ensures critical outputs are reviewed, corrected, and approved before use, with accountability, escalation, and documented decision trails. Automated validators and enrichment help reduce errors but are secondary; recursive chunking is a prompting tactic, not a governance control.
References: AI Security Management (AAISM) Body of Knowledge: Responsible AI & Human Oversight; Generative AI Risk Controls-Approval Workflows and Human Review; AAISM Study Guide: Hallucination Risk Treatment with HITL and Approval Gates.

NEW QUESTION # 203
Which of the following is the PRIMARY purpose of a dedicated AI system policy?

A. Optimizing AI accuracy
B. Ensuring environmental impact is minimized
C. Complying with external regulations
D. Providing a framework to set AI objectives

Answer: D

Explanation:
Per AAISM, an AI policy is a governance instrument that defines objectives, principles, roles, responsibilities, accountability, and control requirements for AI systems across their lifecycle. It establishes the framework within which performance, compliance, ethics, risk appetite, security, privacy, and sustainability objectives are set and operationalized. Environmental considerations (A), accuracy optimization (B), and regulatory compliance (D) are important outcomes addressed under the policy, but the primary purpose is to provide the overarching framework for objectives and controls.
References: AI Security Management (AAISM) Body of Knowledge - AI Governance Frameworks; Policies, Standards, and Procedures; Roles and Accountability in AI Programs.

NEW QUESTION # 204
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